---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: XLM-Roberta_NER
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# XLM-Roberta_NER

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0251
- Precision: 0.9509
- Recall: 0.9721
- F1: 0.9614
- Accuracy: 0.9947

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1665        | 1.0   | 5313  | 0.1461          | 0.7095    | 0.7489 | 0.7287 | 0.9649   |
| 0.0816        | 2.0   | 10626 | 0.0652          | 0.8559    | 0.8845 | 0.8700 | 0.9854   |
| 0.037         | 3.0   | 15939 | 0.0428          | 0.8999    | 0.9380 | 0.9186 | 0.9901   |
| 0.0245        | 4.0   | 21252 | 0.0283          | 0.9463    | 0.9640 | 0.9551 | 0.9941   |
| 0.0193        | 5.0   | 26565 | 0.0251          | 0.9509    | 0.9721 | 0.9614 | 0.9947   |


### Framework versions

- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.2